A visual-search model observer for multislice-multiview SPECT images

Abstract

Purpose: Mathematical model observers are intended for efficient assessment of diagnostic image quality, but model-observer studies often are not representative of clinical realities. Model observers based on a visual-search (VS) paradigm may allow for greater clinical relevance. The author has compared the performances of several VS model observers with those of human observers and an existing scanning model observer for a study involving nodule detection and localization in simulated Tc-99m single-photon emission computed tomography (SPECT) lung volumes.

Methods: A localization receiver operating characteristic (LROC) study compared two iterative SPECT reconstruction strategies: an all-corrections (AllC) strategy with compensations for attenuation, scatter, and distance-dependent camera resolution and an "RC" strategy with resolution compensation only. Nodules in the simulation phantom were of three different relative contrasts. Observers in the study had access to the coronal, sagittal, and transverse displays of the reconstructed volumes. Three human observers each read 50 training volumes and 100 test volumes per reconstruction strategy. The same images were analyzed by a channelized nonprewhitening (CNPW) scanning observer and two VS observers. The VS observers implemented holistic search processes that identified focal points of Tc-99m uptake for subsequent analysis by the CNPW scanning model. The level of prior knowledge about the background structure in the images was a study variable for the model observers. Performance was scored by area under the LROC curve.

Results: The average human-observer performances were respectively 0.67 ± 0.04 and 0.61 ± 0.03 for the RC and AllC strategies. Given approximate knowledge about the background structure, both VS models scored 0.69 ± 0.08 (RC) and 0.66 ± 0.08 (AllC). The scanning observer reversed the strategy ranking in scoring 0.73 ± 0.08 with the AllC strategy and 0.64 ± 0.08 with the RC strategy. The VS observers exhibited less sensitivity to variations in background knowledge compared to the scanning observer.

Conclusions: The VS framework has the potential to increase the clinical similitude of model-observer studies and to enhance the ability of existing model observers to quantitatively predict human-observer performance.

Figure 1

Physical density (top) and NeoTect distribution (bottom) in four transverse slices from the NCAT phantom. Differences in NeoTect uptake have been compressed to enhance visibility. The slice at left includes portions of the liver, spleen, stomach, and heart. The other slices are progressively higher in the torso. The lungs are the dark regions within the torso in the density images.

Figure 2

Comparison of transverse, coronal, and sagittal images extracted from normal reconstructions. Each column pertains to a fixed slice from the NCAT phantom. From top to bottom, the rows represent: (1) noise-free AllC reconstructions; (2) noisy AllC reconstructions; (3) noise-free RC reconstructions; and (4) noisy RC reconstructions.

Figure 3

(a) A 2D CNPW-observer template derived from a set of three square-profile channels. (b) A 3D CNPW-observer template derived from the same channel set for coronal, sagittal, and transverse views. As shown, the horizontal plane applies to the transverse slice. The two vertical planes apply to the coronal and sagittal slices.

Figure 4

Spatial responses for the three difference-of-Gaussian (DOG) channels used in this study.

Figure 5

Computer interface for the human-observer LROC study. An observer uses the horizontal sliders to control the displays in the bottom set of windows. A localization is set by clicking somewhere in one of these windows with the mouse cursor. Confidence ratings are input using the vertical slider. During a training session (shown here), feedback in the form of the noise-free image is provided in the upper set of windows.

Figure 6

Establishing the radius of correct localization r_CL from the human-observer data. The fraction of lesions found for a given strategy is plotted as a function of the proposed radius. The localization data from the three observers were pooled for this analysis, yielding 150 localizations (3 × 50 abnormal volumes) per strategy. The selected value of r_CL is indicated by the arrow.

Figure 7

Search regions for the model observers. At left is a sample transverse slices from an AllC-reconstructed volume. The middle image shows the lung regions (Ω for the scanning observer) as an overlay. The right-hand image indicates the lung focal points determined by the base search.

Figure 8

Comparison of the image processing performed by the model observers for the BKE, BKA, and BAH tasks. Results of the background subtraction carried out for a single test case are shown for the AllC strategy (top row) and the RC strategy (bottom row). At left, subtraction of b reveals a lesion in close proximity to the liver. Lesion detection is less certain with subtraction of the approximate reference image $\widehat{\mathbf{b}}$ (middle column). For the BAH task, there is no background subtraction (right-hand column).